基于事件频率分布比和文档一致性的中国医疗事件检测
Ruirui Han1, Zhichang Zhang1, Hao Wei1
1College of Computer Science and Engineering, Northwest Normal University, 967 Anning East Road, Lanzhou 730070, China.
Mathematical biosciences and engineering : MBE
|June 16, 2023
概括
这项研究引入了一种用于在电子病历 (EMR) 中细粒度检测中国医疗事件的新方法. 该方法通过考虑事件频率分布和文档一致性来提高准确性,优于现有模型.
科学领域:
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 来自电子病历 (EMR) 的结构化医疗事件对于智能诊断和治疗系统至关重要.
- 细粒度的中国医疗事件检测对于构建中国EMRs至关重要.
- 目前的方法没有考虑事件分布特征和文档级一致性.
研究的目的:
- 开发一种用于细粒度检测中国医学事件的新方法.
- 解决现有方法在事件分布和文档一致性方面的局限性.
- 提高中国EMR结构的准确性.
主要方法:
- 中国预培训模型BERT的域名调整,使用大量的中国EMR文本.
- 开发事件频率 - 事件分布比率 (EF-DR) 功能,以捕获不同的事件信息.
- 将EMR文档一致性集成到检测模型中.
主要成果:
- 拟议的方法显著改善了细粒度的中国医学事件检测.
- EF-DR功能有效地利用事件分布特征.
- 整合文档一致性可以提高检测结果.
结论:
- 这种新方法有效地解决了当前中国医疗事件检测的缺陷.
- 这种方法提供了一种更强大,更准确的方法来构建中国EMR.
- 这项工作有助于通过改进EMR数据结构来推进智能诊断和治疗系统.
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